tessl/npm-langchain--langgraph

Low-level orchestration framework for building stateful, multi-actor applications with LLMs

Overview

Eval results

Files

Common Patterns

Name: tessl/npm-langchain--langgraph
Author: tessl

Frequently used patterns and best practices for building LangGraph applications.

State Patterns

Array Accumulation

Collect items from multiple nodes:

const State = Annotation.Root({
  results: Annotation<string[]>({
    reducer: (current, update) => [...current, ...update],
    default: () => []
  })
});

const node1 = (state: State) => ({ results: ["result1"] });
const node2 = (state: State) => ({ results: ["result2"] });

// Final state.results = ["result1", "result2"]

Numeric Aggregation

Sum, multiply, or find max/min:

const State = Annotation.Root({
  total: Annotation<number>({
    reducer: (a, b) => a + b,
    default: () => 0
  }),
  max: Annotation<number>({
    reducer: (a, b) => Math.max(a, b),
    default: () => -Infinity
  })
});

Object Merging

Combine metadata or configuration:

const State = Annotation.Root({
  metadata: Annotation<Record<string, any>>({
    reducer: (a, b) => ({ ...a, ...b }),
    default: () => ({})
  })
});

const node1 = (s: State) => ({ metadata: { source: "api" } });
const node2 = (s: State) => ({ metadata: { timestamp: Date.now() } });

// Final metadata = { source: "api", timestamp: 1234567890 }

Set Operations

Maintain unique collections:

const State = Annotation.Root({
  tags: Annotation<Set<string>>({
    reducer: (a, b) => new Set([...a, ...b]),
    default: () => new Set()
  })
});

Conditional State Updates

Update based on current value:

const State = Annotation.Root({
  score: Annotation<number>({
    reducer: (current, update) => {
      // Only update if new score is higher
      return Math.max(current, update);
    },
    default: () => 0
  })
});

Routing Patterns

Simple Branch

Route based on a condition:

const router = (state: State) => {
  return state.isValid ? "success" : "failure";
};

graph
  .addNode("validate", validateNode)
  .addNode("success", successNode)
  .addNode("failure", failureNode)
  .addConditionalEdges("validate", router, {
    success: "success",
    failure: "failure"
  });

Multi-Way Branching

Route to one of many paths:

const classifier = (state: State) => {
  if (state.type === "urgent") return "highPriority";
  if (state.type === "normal") return "mediumPriority";
  return "lowPriority";
};

graph.addConditionalEdges("classify", classifier, {
  highPriority: "urgentHandler",
  mediumPriority: "normalHandler",
  lowPriority: "lowHandler"
});

Router with Fallback

Always provide a default path:

const router = (state: State) => {
  const handler = state.handlers.get(state.requestType);
  return handler || "default";
};

graph.addConditionalEdges("route", router, {
  typeA: "handlerA",
  typeB: "handlerB",
  default: "defaultHandler"
});

Dynamic Parallel Execution

Use Send for map-reduce patterns:

import { Send } from "@langchain/langgraph";

const fanOut = (state: State) => {
  // Create one Send per item
  return state.items.map(item =>
    new Send("process", { item, results: [] })
  );
};

const graph = new StateGraph(State)
  .addNode("process", processNode)
  .addNode("aggregate", aggregateNode)
  .addConditionalEdges(START, fanOut)
  .addEdge("process", "aggregate")
  .compile();

Workflow Patterns

Sequential Pipeline

Process data through stages:

const graph = new StateGraph(State)
  .addNode("extract", extractNode)
  .addNode("transform", transformNode)
  .addNode("load", loadNode)
  .addEdge(START, "extract")
  .addEdge("extract", "transform")
  .addEdge("transform", "load")
  .addEdge("load", END)
  .compile();

Parallel Processing

Execute independent tasks concurrently:

const graph = new StateGraph(State)
  .addNode("taskA", taskANode)
  .addNode("taskB", taskBNode)
  .addNode("taskC", taskCNode)
  .addNode("combine", combineNode)
  .addEdge(START, "taskA")
  .addEdge(START, "taskB")
  .addEdge(START, "taskC")
  .addEdge("taskA", "combine")
  .addEdge("taskB", "combine")
  .addEdge("taskC", "combine")
  .addEdge("combine", END)
  .compile();

Retry with Max Attempts

const State = Annotation.Root({
  attempts: Annotation<number>({ default: () => 0 }),
  success: Annotation<boolean>({ default: () => false })
});

const shouldRetry = (state: State) => {
  if (state.success) return END;
  if (state.attempts >= 3) return "failed";
  return "retry";
};

const retryNode = (state: State) => ({
  attempts: state.attempts + 1,
  success: Math.random() > 0.5  // Simulated operation
});

graph
  .addNode("retry", retryNode)
  .addNode("failed", failedNode)
  .addEdge(START, "retry")
  .addConditionalEdges("retry", shouldRetry, {
    retry: "retry",
    failed: "failed"
  });

Validation with Recovery

const State = Annotation.Root({
  data: Annotation<any>,
  isValid: Annotation<boolean>(),
  validationErrors: Annotation<string[]>({
    reducer: (a, b) => a.concat(b),
    default: () => []
  })
});

const afterValidation = (state: State) => {
  return state.isValid ? "process" : "fix";
};

graph
  .addNode("validate", validateNode)
  .addNode("fix", autoFixNode)
  .addNode("process", processNode)
  .addConditionalEdges("validate", afterValidation, {
    process: "process",
    fix: "fix"
  })
  .addEdge("fix", "validate");  // Loop back after fix

Agent Patterns

ReAct Loop

Reasoning and acting iteratively:

import { createReactAgent } from "@langchain/langgraph/prebuilt";

const agent = createReactAgent({
  llm: model,
  tools: [searchTool, calculatorTool]
});

// Agent will:
// 1. Reason about the task
// 2. Decide which tool to use
// 3. Execute tool
// 4. Observe result
// 5. Repeat until task complete

Tool-Calling Agent (Manual)

Build your own agent loop:

import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";

const callModel = async (state: MessagesState) => {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
};

const graph = new StateGraph(MessagesAnnotation)
  .addNode("agent", callModel)
  .addNode("tools", new ToolNode(tools))
  .addEdge(START, "agent")
  .addConditionalEdges("agent", toolsCondition)  // Returns "tools" or END
  .addEdge("tools", "agent")
  .compile();

Multi-Agent Collaboration

Multiple specialized agents:

const State = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: messagesStateReducer,
    default: () => []
  }),
  task: Annotation<string>()
});

const router = (state: State) => {
  const lastMessage = state.messages[state.messages.length - 1];
  if (needsResearch(lastMessage)) return "researcher";
  if (needsCoding(lastMessage)) return "coder";
  return END;
};

graph
  .addNode("researcher", researchAgent)
  .addNode("coder", coderAgent)
  .addNode("supervisor", supervisorNode)
  .addEdge(START, "supervisor")
  .addConditionalEdges("supervisor", router, {
    researcher: "researcher",
    coder: "coder"
  })
  .addEdge("researcher", "supervisor")
  .addEdge("coder", "supervisor");

Persistence Patterns

Conversation Memory

import { MemorySaver } from "@langchain/langgraph-checkpoint";

const memory = new MemorySaver();

const chatbot = new StateGraph(MessagesAnnotation)
  .addNode("chat", chatNode)
  .addEdge(START, "chat")
  .addEdge("chat", END)
  .compile({ checkpointer: memory });

// Each user gets their own thread
await chatbot.invoke(
  { messages: [new HumanMessage("Hi")] },
  { configurable: { thread_id: "user-123" } }
);

// Continue conversation
await chatbot.invoke(
  { messages: [new HumanMessage("Tell me more")] },
  { configurable: { thread_id: "user-123" } }
);

State Snapshots

Save and restore from specific points:

// Get current state
const state = await graph.getState({
  configurable: { thread_id: "thread-1" }
});

// Get state history
for await (const snapshot of graph.getStateHistory({
  configurable: { thread_id: "thread-1" }
})) {
  console.log(snapshot.values);
}

// Resume from specific checkpoint
await graph.invoke(null, snapshot.config);

Long-Term Memory

Store data across sessions:

import { InMemoryStore } from "@langchain/langgraph-checkpoint";
import { getStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const node = async (state: State) => {
  const userStore = getStore();

  // Load user preferences
  const prefs = await userStore.get(
    ["users", state.userId],
    "preferences"
  );

  // Save updated preferences
  await userStore.put(
    ["users", state.userId],
    "preferences",
    { theme: "dark", lang: "en" }
  );

  return state;
};

const graph = new StateGraph(State)
  .addNode("process", node)
  .compile({ store });

Error Handling Patterns

Try-Catch in Nodes

const resilientNode = async (state: State) => {
  try {
    const result = await riskyOperation(state.data);
    return { result, error: null };
  } catch (error) {
    return { result: null, error: error.message };
  }
};

Error Recovery Flow

const State = Annotation.Root({
  data: Annotation<any>(),
  error: Annotation<string | null>({ default: () => null })
});

const checkError = (state: State) => {
  return state.error ? "handleError" : "continue";
};

graph
  .addNode("process", processNode)
  .addNode("handleError", errorHandler)
  .addNode("continue", continueNode)
  .addConditionalEdges("process", checkError, {
    handleError: "handleError",
    continue: "continue"
  });

Retry with Backoff

const processWithRetry = task({
  name: "process",
  retry: {
    maxAttempts: 3,
    initialInterval: 1000,
    backoffFactor: 2,
    jitter: true
  }
}, async (data: any) => {
  return await unreliableAPI(data);
});

Streaming Patterns

Progress Updates

import { writer } from "@langchain/langgraph";

const longRunningNode = async (state: State) => {
  writer({ progress: 0, stage: "starting" });

  await step1();
  writer({ progress: 0.33, stage: "step1" });

  await step2();
  writer({ progress: 0.66, stage: "step2" });

  await step3();
  writer({ progress: 1.0, stage: "complete" });

  return { done: true };
};

// Consume progress
for await (const progress of await graph.stream(input, {
  streamMode: "custom"
})) {
  console.log(`${progress.stage}: ${progress.progress * 100}%`);
}

Real-Time UI Updates

for await (const chunk of await graph.stream(input, {
  streamMode: "updates"
})) {
  // chunk = { nodeName: stateUpdate }
  updateUI(chunk);
}

Testing Patterns

Test Individual Nodes

import { describe, it, expect } from "vitest";

describe("processNode", () => {
  it("should increment count", () => {
    const state = { count: 5 };
    const result = processNode(state);
    expect(result.count).toBe(6);
  });
});

Test Graph Execution

it("should complete workflow", async () => {
  const result = await graph.invoke({ count: 0 });
  expect(result.count).toBe(10);
  expect(result.done).toBe(true);
});

Test with Mocks

const mockLLM = {
  invoke: async (messages) => new AIMessage("Mocked response")
};

const agent = createReactAgent({
  llm: mockLLM,
  tools: [mockTool]
});

const result = await agent.invoke({
  messages: [new HumanMessage("Test")]
});

Performance Patterns

Caching Expensive Operations

const expensiveTask = task({
  name: "compute",
  cachePolicy: {
    keyFunc: (input) => JSON.stringify(input),
    ttl: 300000  // 5 minutes
  }
}, async (input: any) => {
  return await expensiveComputation(input);
});

Parallel Execution

Maximize throughput with concurrent nodes:

// These run in parallel automatically
.addEdge(START, "node1")
.addEdge(START, "node2")
.addEdge(START, "node3")

Batch Processing

Process items in batches:

const batchNode = async (state: State) => {
  const batches = chunk(state.items, 10);  // 10 items per batch
  const results = [];

  for (const batch of batches) {
    const batchResults = await Promise.all(
      batch.map(item => processItem(item))
    );
    results.push(...batchResults);
  }

  return { results };
};

Next Steps

API Reference - Explore the complete API
Guides - Read in-depth guides
Examples - See complete working examples
Graph Construction Guide - Build complex workflows
Human-in-the-Loop Guide - Add interrupts and approvals

Install with Tessl CLI

npx tessl i tessl/npm-langchain--langgraph@1.0.1